
import numpy as np 
import pandas as pd 
import seaborn as sns; sns.set(style="ticks", color_codes=True)
import matplotlib.pyplot as plt
import plotly.graph_objs as go
from plotly.subplots import make_subplots
import seaborn as sns
import plotly.express as px
%matplotlib inline


df=pd.read_excel('DataCoSupplyChainDataset.xls')
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
df.head()


df.columns

Index(['Type', 'Days for shipping (real)', 'Days for shipment (scheduled)', 'Benefit per order', 'Sales per customer', 'Delivery Status', 'Late_delivery_risk', 'Category Id', 'Category Name', 'Customer City', 'Customer Country', 'Customer Email', 'Customer Fname', 'Customer Id', 'Customer Lname', 'Customer Password', 'Customer Segment', 'Customer State', 'Customer Street', 'Customer Zipcode', 'Department Id', 'Department Name', 'Latitude', 'Longitude', 'Market', 'Order City', 'Order Country', 'Order Customer Id', 'order date (DateOrders)', 'Order Id', 'Order Item Cardprod Id', 'Order Item Discount', 'Order Item Discount Rate', 'Order Item Id', 'Order Item Product Price', 'Order Item Profit Ratio', 'Order Item Quantity', 'Sales', 'Order Item Total', 'Order Profit Per Order', 'Order Region', 'Order State', 'Order Status', 'Order Zipcode', 'Product Card Id', 'Product Category Id', 'Product Description', 'Product Image', 'Product Name', 'Product Price', 'Product Status',
       'shipping date (DateOrders)', 'Shipping Mode'],
      dtype='object')


data=df.copy()
FeatureList=['Type', 'Benefit per order', 'Sales per customer', 
          'Delivery Status', 'Late_delivery_risk', 'Category Name', 'Customer City', 'Customer Country', 
           'Customer Id', 'Customer Segment', 
          'Customer State', 'Customer Zipcode', 'Department Name', 'Latitude', 'Longitude',
          'Market', 'Order City', 'Order Country', 'Order Customer Id', 'order date (DateOrders)', 'Order Id', 
          'Order Item Cardprod Id', 'Order Item Discount', 'Order Item Discount Rate', 'Order Item Id', 
          'Order Item Product Price', 'Order Item Profit Ratio', 'Order Item Quantity', 'Sales', 'Order Item Total', 
          'Order Profit Per Order', 'Order Region', 'Order State', 'Order Status', 'Order Zipcode', 'Product Card Id',
          'Product Category Id', 'Product Description', 'Product Image', 'Product Name', 'Product Price', 'Product Status',
       'shipping date (DateOrders)', 'Shipping Mode']

df1=df[FeatureList]
df1.head()


data_delivery_status=df1.groupby(['Delivery Status'])['Order Id'].count().reset_index(name='Number of Orders').sort_values(by= 'Number of Orders', ascending= False)
px.bar(x=data_delivery_status['Delivery Status'] , y=data_delivery_status['Number of Orders']  , color=data_delivery_status['Number of Orders'],
      labels = { 'Delivery Status': 'Delivery Status', 'Number of Orders': 'Number of Orders'})


data_delivery_status_region=df1.groupby(['Delivery Status', 'Order Region'])['Order Id'].count().reset_index(name='Number of Orders').sort_values(by= 'Number of Orders', ascending= False)
px.bar(data_delivery_status_region, x='Delivery Status', y='Number of Orders'  , color='Order Region',
      )


df1['Customer_ID_STR']=df1['Customer Id'].astype(str)

data_customers=df1.groupby(['Customer_ID_STR'])['Order Id'].count().reset_index(name='Number of Orders').sort_values(by= 'Number of Orders', ascending= False)
px.bar(data_customers.head(20),x='Number of Orders', y='Customer_ID_STR' , color='Number of Orders'      )

/tmp/ipykernel_1492430/1113062083.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


df1['Customer_ID_STR']=df1['Customer Id'].astype(str)

data_customers_profit=df1.groupby(['Customer_ID_STR'])['Order Profit Per Order'].sum().reset_index(name='Profit of Orders').sort_values(by= 'Profit of Orders', ascending= False)
px.bar(data_customers_profit.head(20),x='Profit of Orders', y='Customer_ID_STR' , color='Profit of Orders'      )

/tmp/ipykernel_1492430/34035558.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


#Customer Segments
data_Customer_Segment=df1.groupby(['Customer Segment'])['Order Id'].count().reset_index(name='Number of Orders').sort_values(by= 'Number of Orders', ascending= False)
px.pie(data_Customer_Segment, values='Number of Orders', names= 'Customer Segment' , title= 'Number of Orders of different Customer Segments', 
       width=600 , height=600 , color_discrete_sequence = px.colors.sequential.RdBu)


data_Category_Name=df1.groupby(['Category Name'])['Order Id'].count().reset_index(name='Number of Orders').sort_values(by= 'Number of Orders', ascending= True)
px.bar(data_Category_Name, x='Number of Orders',y = 'Category Name',color ='Number of Orders')


data_Region=df1.groupby(['Order Region'])['Order Id'].count().reset_index(name='Number of Orders').sort_values(by= 'Number of Orders', ascending= True)
px.bar(data_Region, x='Number of Orders',y = 'Order Region',color ='Number of Orders')


data_countries=df1.groupby(['Order Country'])['Order Id'].count().reset_index(name='Number of Orders').sort_values(by= 'Number of Orders', ascending= True)
px.bar(data_countries.head(20), x='Number of Orders',y = 'Order Country',color ='Number of Orders')


df_geo=df1.groupby([ 'Order Country', 'Order City'])['Order Profit Per Order'].sum().reset_index(name='Profit of Orders').sort_values(by= 'Profit of Orders', ascending= False)
df_geo


fig = px.choropleth(df_geo ,  locationmode='country names', locations='Order Country',
                    color='Profit of Orders', 
                    hover_name='Order Country', 
                    color_continuous_scale=px.colors.sequential.Plasma)

fig.show()


df_sales_country=df1.groupby([ 'Order Country'])['Sales'].sum().reset_index(name='Sales of Orders').sort_values(by= 'Sales of Orders', ascending= False)
px.bar(df_sales_country.head(10), x='Sales of Orders',y = 'Order Country',color ='Sales of Orders')


df_sales_country=df1.groupby([ 'Order Country'])['Sales'].sum().reset_index(name='Sales of Orders').sort_values(by= 'Sales of Orders', ascending= False)
px.bar(df_sales_country.head(10), x='Sales of Orders',y = 'Order Country',color ='Sales of Orders')


df_sales_country=df1.groupby([ 'Product Name'])['Sales'].sum().reset_index(name='Sales of Orders').sort_values(by= 'Sales of Orders', ascending= False)
px.bar(df_sales_country.head(10), x='Sales of Orders',y = 'Product Name',color ='Sales of Orders')


df_sales_pd=df1.groupby([ 'Product Name', 'Delivery Status'])['Sales'].sum().reset_index(name='Sales of Orders').sort_values(by= 'Sales of Orders', ascending= False)
px.bar(df_sales_pd.head(10), x='Sales of Orders',y = 'Product Name',color ='Delivery Status')


df_sales_pr=df1.groupby([ 'Product Name', 'Order Region'])['Sales'].sum().reset_index(name='Sales of Orders').sort_values(by= 'Sales of Orders', ascending= False)
px.bar(df_sales_pr.head(10), x='Sales of Orders',y = 'Product Name',color ='Order Region')


df_sales_pr=df1.groupby([  'Category Name'])['Sales'].sum().reset_index(name='Sales of Orders').sort_values(by= 'Sales of Orders', ascending= False)
px.bar(df_sales_pr.head(10), x='Sales of Orders',y = 'Category Name',color ='Sales of Orders')


df_sales_pr=df1.groupby([ 'Type'])['Sales'].sum().reset_index(name='Sales of Orders').sort_values(by= 'Sales of Orders', ascending= False)
px.bar(df_sales_pr.head(10), x='Sales of Orders',y = 'Type',color ='Sales of Orders')


df_sales_tp=df1.groupby([ 'Type', 'Product Name'])['Sales'].sum().reset_index(name='Sales of Orders').sort_values(by= 'Sales of Orders', ascending= False)
px.bar(df_sales_tp.head(10), x='Sales of Orders',y = 'Type',color ='Product Name')


import datetime as dt
data_orderdate=df[['order date (DateOrders)', 'Sales']]
data_orderdate['order_date'] = pd.to_datetime(data_orderdate['order date (DateOrders)'])

/tmp/ipykernel_1492430/561001720.py:3: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


data_orderdate["Quarter"] = data_orderdate['order_date'].dt.quarter
data_orderdate["Month"] = data_orderdate['order_date'].dt.month
data_orderdate["year"] = data_orderdate['order_date'].dt.year

/tmp/ipykernel_1492430/3482627655.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/tmp/ipykernel_1492430/3482627655.py:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/tmp/ipykernel_1492430/3482627655.py:3: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


data_orderdate['YearStr']=data_orderdate['year'].astype(str)
df_sales_year=data_orderdate.groupby([ 'YearStr'])['Sales'].sum().reset_index(name='Sales of Orders').sort_values(by= 'Sales of Orders', ascending= False)
px.bar(df_sales_year, x='Sales of Orders',y = 'YearStr',color ='Sales of Orders')

/tmp/ipykernel_1492430/3922562175.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


data_orderdate['QuarterStr']=data_orderdate['Quarter'].astype(str)
df_sales_quarter=data_orderdate.groupby([ 'YearStr','QuarterStr'])['Sales'].sum().reset_index(name='Sales of Orders').sort_values(by= 'Sales of Orders', ascending= False)
px.bar(df_sales_quarter, x='Sales of Orders',y = 'QuarterStr',color ='YearStr')

/tmp/ipykernel_1492430/1609770672.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


data_orderdate['MonthStr']=data_orderdate['Month'].astype(str)
df_sales_m=data_orderdate.groupby([ 'QuarterStr', 'MonthStr'])['Sales'].sum().reset_index(name='Sales of Orders').sort_values(by= 'Sales of Orders', ascending= False)
px.bar(df_sales_m, x='Sales of Orders',y = 'QuarterStr',color ='MonthStr')

/tmp/ipykernel_1492430/1879916405.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


data=df1.copy()
data['SUSPECTED_FRAUD'] = np.where(data['Order Status'] == 'SUSPECTED_FRAUD', 1, 0)


from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()
def Labelencoder_feature(x):
    le=LabelEncoder()
    x=le.fit_transform(x)
    return x


features=data.drop(columns=['SUSPECTED_FRAUD','Order Status' ])
target=data['SUSPECTED_FRAUD']


features.isnull().sum()

Type                               0
Benefit per order                  0
Sales per customer                 0
Delivery Status                    0
Late_delivery_risk                 0
Category Name                      0
Customer City                      0
Customer Country                   0
Customer Id                        0
Customer Segment                   0
Customer State                     0
Customer Zipcode                   3
Department Name                    0
Latitude                           0
Longitude                          0
Market                             0
Order City                         0
Order Country                      0
Order Customer Id                  0
order date (DateOrders)            0
Order Id                           0
Order Item Cardprod Id             0
Order Item Discount                0
Order Item Discount Rate           0
Order Item Id                      0
Order Item Product Price           0
Order Item Profit Ratio            0
Order Item Quantity                0
Sales                              0
Order Item Total                   0
Order Profit Per Order             0
Order Region                       0
Order State                        0
Order Zipcode                 155679
Product Card Id                    0
Product Category Id                0
Product Description           180519
Product Image                      0
Product Name                       0
Product Price                      0
Product Status                     0
shipping date (DateOrders)         0
Shipping Mode                      0
Customer_ID_STR                    0
dtype: int64


features=features.apply(Labelencoder_feature)
features.head()


data1=pd.concat([features,target],axis=1)


corr = data1.corr()
columns = np.full((corr.shape[0],), True, dtype=bool)
for i in range(corr.shape[0]):
    for j in range(i+1, corr.shape[0]):
        if corr.iloc[i,j] >= 0.8:
            if columns[j]:
                columns[j] = False
selected_columns = data1.columns[columns]
selected_columns

Index(['Type', 'Benefit per order', 'Sales per customer', 'Delivery Status', 'Late_delivery_risk', 'Category Name', 'Customer City', 'Customer Country', 'Customer Id', 'Customer Segment', 'Customer State', 'Customer Zipcode', 'Department Name', 'Latitude', 'Longitude', 'Market', 'Order City', 'Order Country', 'order date (DateOrders)', 'Order Id', 'Order Item Cardprod Id', 'Order Item Discount', 'Order Item Discount Rate', 'Order Item Product Price', 'Order Item Quantity', 'Order Region', 'Order State', 'Order Zipcode', 'Product Description', 'Product Image', 'Product Status', 'Shipping Mode', 'Customer_ID_STR', 'SUSPECTED_FRAUD'], dtype='object')


features1=features[['Type', 'Benefit per order', 'Sales per customer', 'Delivery Status', 'Late_delivery_risk', 
                    'Category Name', 'Customer City', 'Customer Country', 'Customer Id', 'Customer Segment', 
                    'Customer State', 'Customer Zipcode', 'Department Name', 'Latitude', 'Longitude', 'Market', 
                    'Order City', 'Order Country', 'order date (DateOrders)', 'Order Id', 'Order Item Cardprod Id',
                    'Order Item Discount', 'Order Item Discount Rate', 'Order Item Product Price', 'Order Item Quantity',
                    'Order Region', 'Order State', 'Order Zipcode', 'Product Description', 'Product Image', 
                    'Product Status', 'Shipping Mode', 'Customer_ID_STR']]


from scipy.stats import pearsonr

corre=pd.DataFrame()

for i in features1.columns:
    corre[i]= pearsonr(target, features1[i])
    
    
corre

/opt/conda/lib/python3.7/site-packages/scipy/stats/stats.py:3913: PearsonRConstantInputWarning:

An input array is constant; the correlation coefficent is not defined.


corre1=corre.T


coore2= corre1.iloc[:,0].sort_values(ascending=False)
coore2

Type                        0.202094
Delivery Status             0.128768
Customer Id                 0.009375
Order Region                0.009077
Customer Country            0.006756
Customer State              0.005670
Longitude                   0.005100
order date (DateOrders)     0.003002
Order Item Discount         0.002893
Order Item Discount Rate    0.002271
Order Id                    0.002222
Customer_ID_STR             0.000490
Order Item Product Price    0.000315
Market                     -0.000293
Product Image              -0.000741
Order Item Quantity        -0.000757
Sales per customer         -0.000807
Department Name            -0.000837
Order Item Cardprod Id     -0.001109
Category Name              -0.002104
Benefit per order          -0.002586
Customer City              -0.003561
Shipping Mode              -0.004641
Latitude                   -0.004709
Order Country              -0.004928
Customer Zipcode           -0.005600
Order City                 -0.005889
Customer Segment           -0.005935
Order State                -0.006105
Order Zipcode              -0.042742
Product Description        -0.056158
Late_delivery_risk         -0.167158
Product Status                   NaN
Name: 0, dtype: float64


coore2.index

Index(['Type', 'Delivery Status', 'Customer Id', 'Order Region', 'Customer Country', 'Customer State', 'Longitude', 'order date (DateOrders)', 'Order Item Discount', 'Order Item Discount Rate', 'Order Id', 'Customer_ID_STR', 'Order Item Product Price', 'Market', 'Product Image', 'Order Item Quantity', 'Sales per customer', 'Department Name', 'Order Item Cardprod Id', 'Category Name', 'Benefit per order', 'Customer City', 'Shipping Mode', 'Latitude', 'Order Country', 'Customer Zipcode', 'Order City', 'Customer Segment', 'Order State', 'Order Zipcode', 'Product Description', 'Late_delivery_risk', 'Product Status'], dtype='object')


new_features= ['Type', 'Delivery Status', 'Order Region', 'Customer Country', 'Customer State', 'Order Zipcode',
               'Shipping Mode', 'Order Country', 'Customer Zipcode', 'Order City', 'Customer Segment', 'Order State',
               'Late_delivery_risk', 'Product Description', 'Product Status']


#Feature Selection
from sklearn.feature_selection import f_regression
F_values, p_values  = f_regression(features, target)

/opt/conda/lib/python3.7/site-packages/sklearn/feature_selection/_univariate_selection.py:302: RuntimeWarning:

invalid value encountered in true_divide


import itertools
f_reg_results = [(i, v, z) for i, v, z in itertools.zip_longest(features.columns, F_values,  ['%.3f' % p for p in p_values])]
f_reg_results=pd.DataFrame(f_reg_results, columns=['Variable','F_Value', 'P_Value'])


f_reg_results=pd.DataFrame(f_reg_results, columns=['Variable','F_Value', 'P_Value'])
f_reg_results = f_reg_results.sort_values(by=['P_Value'])
f_reg_results.P_Value= f_reg_results.P_Value.astype(float)
f_reg_results=f_reg_results[f_reg_results.P_Value<0.06]
f_reg_results


f_reg_list=f_reg_results.Variable.values
f_reg_list

array(['Type', 'Order Customer Id', 'Delivery Status',
       'Late_delivery_risk', 'Order Zipcode', 'Customer Id',
       'Order Region', 'Product Description', 'Customer Country',
       'Order State', 'Order City', 'Customer Segment', 'Customer State',
       'Customer Zipcode', 'Longitude', 'Order Country', 'Latitude',
       'Shipping Mode', 'shipping date (DateOrders)'], dtype=object)


final_features=features[['Type', 'Order Region', 'Delivery Status', 'Late_delivery_risk',
       'Customer Country', 'Order State', 'Order City',
       'Customer Segment', 'Customer State', 'Customer Zipcode',
       'Order Country', 'Order Zipcode', 'shipping date (DateOrders)',
       'Shipping Mode']]


final_data=pd.concat([final_features, target], axis=1)


fig = plt.figure(figsize=(20,10))
sns.heatmap(final_data.corr(), annot = True, fmt = '.2f', cmap = 'magma')

<AxesSubplot:>


final_features2=final_features.drop(columns=['Customer State', 'Customer Zipcode'])


from sklearn.pipeline import make_pipeline
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import cross_val_score, cross_validate
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import StandardScaler
from xgboost import XGBClassifier
from sklearn.metrics import *
from sklearn import metrics
from sklearn.metrics import classification_report


from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(final_features2, target, test_size = 0.2, random_state = 42)


lgr_pipline  = Pipeline([("scaler", StandardScaler()), ("LogisticRegression", LogisticRegression())])
rfc_pipline = Pipeline([("scaler", StandardScaler()), ("RandomForestClassifier", RandomForestClassifier())])
knn_pipline = Pipeline([("scaler", StandardScaler()), ("KNeighborsClassifier", KNeighborsClassifier())])
gnb_pipline = Pipeline([("scaler", StandardScaler()), ("GaussianNB", GaussianNB())])
sgd_pipline = Pipeline([("scaler", StandardScaler()), ("SGDClassifier", SGDClassifier())])
dt_pipline = Pipeline([("scaler", StandardScaler()), ("DecisionTreeClassifier", DecisionTreeClassifier())])


pip_dict1 ={0:'Logistic Regression' , 1:'RandomForestClassifier' , 2: 'KNeighborsClassifier' ,
            3: 'GaussianNB', 4: 'SGDClassifier', 5: 'DecisionTreeClassifier' }


piplines1=[lgr_pipline, rfc_pipline , knn_pipline, gnb_pipline  , sgd_pipline , dt_pipline ]


scores_df = pd.DataFrame(columns = ["Model", "CVScores"])
for i, pipe in enumerate(piplines1):
    score = cross_val_score(pipe, final_features2, target, cv = 10)
    print(pip_dict1[i], ": ", score.mean())

Logistic Regression :  0.9751217455582278
RandomForestClassifier :  0.9744182816313967
KNeighborsClassifier :  0.9748503332686301
GaussianNB :  0.9795478562150082
SGDClassifier :  0.9774982135396149
DecisionTreeClassifier :  0.9721525740097521


grid_params = [
    {"classifier": [RandomForestClassifier()],
    "classifier__n_estimators": [50,100,150,200,250,300],
    "classifier__criterion": ["gini", "entropy"],
    "classifier__max_features": ["auto", "sqrt", "log2"],
    },
]


pipeline_new = Pipeline([("scaler", StandardScaler()), ("classifier", RandomForestClassifier())]) 
random_search = RandomizedSearchCV(estimator = pipeline_new, param_distributions = grid_params, scoring = 'neg_mean_absolute_error', n_jobs= -1, cv = 8, verbose = 10, random_state = 42)


best_model = random_search.fit(X_train, y_train)

Fitting 8 folds for each of 10 candidates, totalling 80 fits

[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:  1.5min
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:  1.7min
[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:  2.2min
[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:  2.7min
[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  3.3min
[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:  4.1min
[Parallel(n_jobs=-1)]: Done  53 tasks      | elapsed:  5.2min
[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:  5.6min
[Parallel(n_jobs=-1)]: Done  80 out of  80 | elapsed:  7.3min finished


best_model.best_params_

{'classifier__n_estimators': 250,
 'classifier__max_features': 'log2',
 'classifier__criterion': 'gini',
 'classifier': RandomForestClassifier(max_features='log2', n_estimators=250)}


pipeline_rf = Pipeline([('scaler', StandardScaler()), 
                           ('Random Forest Calssifer', RandomForestClassifier(criterion='entropy', max_features='sqrt',
                        n_estimators=150))])


model = pipeline_rf.fit(X_train, y_train)


rf_train_predict = pd.DataFrame({'actual' : y_train,
                                 'predicted' : model.predict(X_train)})
rf_train_predict.head()


rf_test_predict = pd.DataFrame({'actual' : y_test,
                                 'predicted' : model.predict(X_test)})
rf_test_predict.head()


print('Accuracy Score for train dataset : ' , metrics.accuracy_score(rf_train_predict.actual, rf_train_predict.predicted))
print('Accuracy Score for test dataset : ' , metrics.accuracy_score(rf_test_predict.actual, rf_test_predict.predicted))

Accuracy Score for train dataset :  1.0
Accuracy Score for test dataset :  0.9960669177930424


print('ROC-AUC Score for train dataset : ' , metrics.roc_auc_score(rf_train_predict.actual, rf_train_predict.predicted))
print('ROC-AUC Score for validation dataset : ' , metrics.roc_auc_score(rf_test_predict.actual, rf_test_predict.predicted))

ROC-AUC Score for train dataset :  1.0
ROC-AUC Score for validation dataset :  0.9491915810971804


conn_cm_test = metrics.confusion_matrix(rf_test_predict.actual, rf_test_predict.predicted, [1,0])
sns.heatmap(conn_cm_test, fmt= '.2f', annot=True)

/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:70: FutureWarning:

Pass labels=[1, 0] as keyword args. From version 0.25 passing these as positional arguments will result in an error

<AxesSubplot:>


print(metrics.classification_report(rf_test_predict.actual, rf_test_predict.predicted))

              precision    recall  f1-score   support

           0       1.00      1.00      1.00     35254
           1       0.93      0.90      0.92       850

    accuracy                           1.00     36104
   macro avg       0.96      0.95      0.96     36104
weighted avg       1.00      1.00      1.00     36104


data=df[['Type', 'Benefit per order', 'Sales per customer', 'Delivery Status', 'Late_delivery_risk', 'Category Name', 
             'Customer City', 'Customer Country', 'Customer Segment', 'Customer State', 'Customer Zipcode', 
             'Department Name', 'Market', 'Order City', 'Order Country', 'Customer Id',
             'order date (DateOrders)', 'Order Item Cardprod Id', 'Order Item Discount', 'Order Item Discount Rate',
             'Order Item Id', 'Order Item Profit Ratio', 'Sales', 'Order Status',
             'Order Item Total', 'Order Profit Per Order', 'Order Region', 'Order State', 'Order Zipcode', 
             'Product Card Id', 'Product Category Id', 'Product Description', 'Product Image', 'Product Name', 'Product Price',
             'Product Status', 'shipping date (DateOrders)', 'Shipping Mode']]


features=data.drop(columns=['Late_delivery_risk'])
target=data['Late_delivery_risk']


features=features.apply(Labelencoder_feature)
features.head()


#Feature Selection
from sklearn.feature_selection import f_regression
F_values, p_values  = f_regression(features, target)

/opt/conda/lib/python3.7/site-packages/sklearn/feature_selection/_univariate_selection.py:302: RuntimeWarning:

invalid value encountered in true_divide


import itertools
f_reg_results = [(i, v, z) for i, v, z in itertools.zip_longest(features.columns, F_values,  ['%.3f' % p for p in p_values])]
f_reg_results=pd.DataFrame(f_reg_results, columns=['Variable','F_Value', 'P_Value'])


f_reg_results=pd.DataFrame(f_reg_results, columns=['Variable','F_Value', 'P_Value'])
f_reg_results = f_reg_results.sort_values(by=['P_Value'])
f_reg_results.P_Value= f_reg_results.P_Value.astype(float)
f_reg_results=f_reg_results[f_reg_results.P_Value<0.06]
f_reg_results


f_reg_list=f_reg_results.Variable.values
f_reg_list

array(['Type', 'Product Description', 'Order Zipcode', 'Shipping Mode',
       'Delivery Status', 'Order Region', 'Customer City',
       'shipping date (DateOrders)'], dtype=object)


df['Delivery Status'].value_counts()

Late delivery        98977
Advance shipping     41592
Shipping on time     32196
Shipping canceled     7754
Name: Delivery Status, dtype: int64


final_features=features[['Type', 'Shipping Mode', 'Order Region',
       'Customer City', 'shipping date (DateOrders)']]


final_data=pd.concat([final_features, target], axis=1)


fig = plt.figure(figsize=(20,10))
sns.heatmap(final_data.corr(), annot = True, fmt = '.2f', cmap = 'magma')

<AxesSubplot:>


from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(final_features, target, test_size = 0.2, random_state = 42)


scores_df = pd.DataFrame(columns = ["Model", "CVScores"])
for i, pipe in enumerate(piplines1):
    score = cross_val_score(pipe, final_features, target, cv = 10)
    print(pip_dict1[i], ": ", score.mean())

Logistic Regression :  0.6906918386309326
RandomForestClassifier :  0.8928088127536521
KNeighborsClassifier :  0.7423094244520747
GaussianNB :  0.6718960851534189
SGDClassifier :  0.6906918386309326
DecisionTreeClassifier :  0.885873228084354


grid_params = [
    {"classifier": [RandomForestClassifier()],
    "classifier__n_estimators": [50,100,150,200,250,300],
    "classifier__criterion": ["gini", "entropy"],
    "classifier__max_features": ["auto", "sqrt", "log2"],
    },
    
    {"classifier": [KNeighborsClassifier()],
     "classifier__n_neighbors": [2,3,4,5,6],
     "classifier__algorithm": ['auto', 'ball_tree', 'kd_tree', 'brute'],
     "classifier__leaf_size": [10,20,30,40,50],
    },
    
    {"classifier": [DecisionTreeClassifier()],
    "classifier__splitter" :["best", "random"],
    "classifier__criterion": ["gini", "entropy"],
    "classifier__max_features": ["auto", "sqrt", "log2"],
    },

]


pipeline_new = Pipeline([("scaler", StandardScaler()), ("classifier", RandomForestClassifier())])

random_search = RandomizedSearchCV(estimator = pipeline_new, param_distributions = grid_params, scoring = 'neg_mean_absolute_error', n_jobs= -1, cv = 8, verbose = 10, random_state = 42)


best_model = random_search.fit(X_train, y_train)

Fitting 8 folds for each of 10 candidates, totalling 80 fits

[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:  2.6min
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:  2.7min
[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:  2.8min
[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:  2.8min
/opt/conda/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py:691: UserWarning:

A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.

[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  4.8min
[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:  5.3min
[Parallel(n_jobs=-1)]: Done  53 tasks      | elapsed:  5.9min
[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:  8.1min
[Parallel(n_jobs=-1)]: Done  80 out of  80 | elapsed: 11.4min finished


best_model.best_params_

{'classifier__n_estimators': 200,
 'classifier__max_features': 'sqrt',
 'classifier__criterion': 'gini',
 'classifier': RandomForestClassifier(max_features='sqrt', n_estimators=200)}


pipeline_rfl = Pipeline([('scaler', StandardScaler()), 
                           ('andomForestClassifier', RandomForestClassifier(criterion='entropy'))])


model_rfl = pipeline_rfl.fit(X_train, y_train)


rfl_train_predict = pd.DataFrame({'actual' : y_train,
                                 'predicted' : model_rfl.predict(X_train)})
rfl_train_predict.head()


rfl_test_predict = pd.DataFrame({'actual' : y_test,
                                 'predicted' : model_rfl.predict(X_test)})
rfl_test_predict.head()


print('Accuracy Score for train dataset : ' , metrics.accuracy_score(rfl_train_predict.actual, rfl_train_predict.predicted))
print('Accuracy Score for test dataset : ' , metrics.accuracy_score(rfl_test_predict.actual, rfl_test_predict.predicted))

Accuracy Score for train dataset :  0.9999792265346398
Accuracy Score for test dataset :  0.9481497894970087


print('ROC-AUC Score for train dataset : ' , metrics.roc_auc_score(rfl_train_predict.actual, rfl_train_predict.predicted))
print('ROC-AUC Score for validation dataset : ' , metrics.roc_auc_score(rfl_test_predict.actual, rfl_test_predict.predicted))

ROC-AUC Score for train dataset :  0.9999797059508928
ROC-AUC Score for validation dataset :  0.9478228827901538


conn_cm_test = metrics.confusion_matrix(rfl_test_predict.actual, rfl_test_predict.predicted, [1,0])
sns.heatmap(conn_cm_test, fmt= '.2f', annot=True)

/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:70: FutureWarning:

Pass labels=[1, 0] as keyword args. From version 0.25 passing these as positional arguments will result in an error

<AxesSubplot:>


print(metrics.classification_report(rfl_test_predict.actual, rfl_test_predict.predicted))

              precision    recall  f1-score   support

           0       0.94      0.94      0.94     16307
           1       0.95      0.95      0.95     19797

    accuracy                           0.95     36104
   macro avg       0.95      0.95      0.95     36104
weighted avg       0.95      0.95      0.95     36104


data_sales=df[['Type', 'Benefit per order', 'Sales per customer', 
          'Delivery Status', 'Late_delivery_risk', 'Category Name', 'Customer City', 'Customer Country', 
           'Customer Id', 'Customer Segment', 
          'Customer State', 'Customer Zipcode', 'Department Name', 'Latitude', 'Longitude',
          'Market', 'Order City', 'Order Country', 'Order Customer Id', 'order date (DateOrders)', 'Order Id', 
          'Order Item Cardprod Id', 'Order Item Discount', 'Order Item Discount Rate', 'Order Item Id', 
          'Order Item Product Price', 'Order Item Profit Ratio', 'Order Item Quantity', 'Sales', 'Order Item Total', 
          'Order Profit Per Order', 'Order Region', 'Order State', 'Order Status', 'Order Zipcode', 'Product Card Id',
          'Product Category Id', 'Product Description', 'Product Image', 'Product Name', 'Product Price', 'Product Status',
       'shipping date (DateOrders)', 'Shipping Mode']]


features=data_sales.drop(columns=['Sales', 'Order Item Quantity', 'Order Item Product Price'])
target=data_sales['Sales']


features=features.apply(Labelencoder_feature)
features.head()


#Feature Selection based on importance
from sklearn.feature_selection import f_regression
F_values, p_values  = f_regression(features, target)

/opt/conda/lib/python3.7/site-packages/sklearn/feature_selection/_univariate_selection.py:302: RuntimeWarning:

invalid value encountered in true_divide


import itertools
f_reg_results = [(i, v, z) for i, v, z in itertools.zip_longest(features.columns, F_values,  ['%.3f' % p for p in p_values])]
f_reg_results=pd.DataFrame(f_reg_results, columns=['Variable','F_Value', 'P_Value'])


f_reg_results=pd.DataFrame(f_reg_results, columns=['Variable','F_Value', 'P_Value'])
f_reg_results = f_reg_results.sort_values(by=['P_Value'])
f_reg_results.P_Value= f_reg_results.P_Value.astype(float)
f_reg_results=f_reg_results[f_reg_results.P_Value<0.06]
f_reg_results


f_reg_list=f_reg_results.Variable.values
f_reg_list

array(['Order Id', 'Order Item Discount', 'Order Item Cardprod Id',
       'shipping date (DateOrders)', 'order date (DateOrders)',
       'Order Customer Id', 'Order Profit Per Order', 'Market',
       'Order Region', 'Order State', 'Order Item Total',
       'Department Name', 'Product Card Id', 'Customer Id',
       'Product Category Id', 'Product Image', 'Category Name',
       'Product Name', 'Product Price', 'Sales per customer',
       'Benefit per order', 'Order Item Id', 'Order City',
       'Customer Segment', 'Order Zipcode'], dtype=object)


final_features=features[['Order Id', 'Order Item Discount', 'Order Item Cardprod Id',
       'shipping date (DateOrders)', 'order date (DateOrders)',
       'Order Customer Id', 'Order Profit Per Order', 'Market',
       'Order Region', 'Order State', 'Order Item Total',
       'Department Name', 'Product Card Id', 'Customer Id',
       'Product Category Id', 'Product Image', 'Category Name',
       'Product Name', 'Product Price', 'Sales per customer',
       'Benefit per order', 'Order Zipcode', 'Order Item Id',
       'Order City', 'Customer Segment']]


final_data=pd.concat([final_features, target], axis=1)


fig = plt.figure(figsize=(20,10))
sns.heatmap(final_data.corr(), annot = True, fmt = '.2f', cmap = 'magma')

<AxesSubplot:>


from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV

from sklearn.metrics import *
from sklearn.linear_model import LinearRegression, RANSACRegressor, Lasso, Ridge, SGDRegressor
from sklearn.ensemble import RandomForestRegressor
from xgboost import XGBRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor


from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(final_features, target, test_size = 0.3, random_state = 42)


lr_pipeline = Pipeline([("scaler", StandardScaler()), ("linear_regression", LinearRegression())])
ridge_pipeline = Pipeline([("scaler", StandardScaler()), ("ridge_regressor", Ridge(random_state = 42))])
lasso_pipeline = Pipeline([("scaler", StandardScaler()), ("lasso_regressor", Lasso(random_state = 42))])
random_forest_pipeline = Pipeline([("scaler", StandardScaler()), ("randomforest_regression", RandomForestRegressor(random_state = 42))])
xgboost_pipeline = Pipeline([("scaler", StandardScaler()), ("xgboost_regression", XGBRegressor())])
knn_pipeline = Pipeline([("scaler", StandardScaler()), ("knn_regression", KNeighborsRegressor())])


pipelines = [lr_pipeline, ridge_pipeline, lasso_pipeline,
            random_forest_pipeline, xgboost_pipeline, knn_pipeline]


pipe_dict = {0: "Linear Regression", 1: "Ridge",
            2: "Lasso", 3: "RandomForest", 4: "XGBoost",
            5: "Decision Tree", 6: "KNN"}


scores_df = pd.DataFrame(columns = ["Model", "CVScores"])
for i, pipe in enumerate(pipelines):
    score = cross_val_score(pipe, final_features,target, cv = 5)
    print(pipe_dict[i], ": ", score.mean())

Linear Regression :  0.7852647150644007
Ridge :  0.785264849136577
Lasso :  0.7805693158852282
RandomForest :  0.9995414205129233
XGBoost :  0.999651640861491
Decision Tree :  0.9333894519285579


grid_params = [
{"classifier": [XGBRegressor()],
     "classifier__n_estimators": [100,150,200,250,300],    
}
]


pipeline_new = Pipeline([("scaler", StandardScaler()), ("classifier", XGBRegressor())])
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV


random_search = RandomizedSearchCV(estimator = pipeline_new, param_distributions = grid_params, scoring = 'neg_mean_absolute_error', n_jobs= -1, cv = 8, verbose = 10, random_state = 42)


best_model = random_search.fit(X_train, y_train)

Fitting 8 folds for each of 5 candidates, totalling 40 fits

/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_search.py:282: UserWarning:

The total space of parameters 5 is smaller than n_iter=10. Running 5 iterations. For exhaustive searches, use GridSearchCV.

[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:  5.1min
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:  8.9min
[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed: 17.8min
[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed: 23.0min
[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed: 43.7min
[Parallel(n_jobs=-1)]: Done  38 out of  40 | elapsed: 51.5min remaining:  2.7min
[Parallel(n_jobs=-1)]: Done  40 out of  40 | elapsed: 51.5min finished


best_model.best_params_

{'classifier__n_estimators': 300,
 'classifier': XGBRegressor(base_score=None, booster=None, colsample_bylevel=None,
              colsample_bynode=None, colsample_bytree=None, gamma=None,
              gpu_id=None, importance_type='gain', interaction_constraints=None,
              learning_rate=None, max_delta_step=None, max_depth=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=300, n_jobs=None, num_parallel_tree=None,
              random_state=None, reg_alpha=None, reg_lambda=None,
              scale_pos_weight=None, subsample=None, tree_method=None,
              validate_parameters=None, verbosity=None)}


pipeline_XGBRegressor = Pipeline([('scaler', StandardScaler()), ('XGBRegressor',  XGBRegressor(importance_type='gain', n_estimators=300, ))])


model = pipeline_XGBRegressor.fit(X_train, y_train)


XGB_train_predict = pd.DataFrame({'actual' : y_train,
                                 'predicted' : model.predict(X_train)})
XGB_train_predict.head()


XGB_test_predict = pd.DataFrame({'actual' : y_test,
                                 'predicted' : model.predict(X_test)})
XGB_test_predict.head()


predict = model.predict(X_test)


r2_score(y_test, predict, multioutput='uniform_average')

0.9974757060247059


!ls ../input
!git

dataco-smart-supply-chain-for-big-data-analysis
usage: git [--version] [--help] [-C <path>] [-c <name>=<value>]
           [--exec-path[=<path>]] [--html-path] [--man-path] [--info-path]
           [-p | --paginate | --no-pager] [--no-replace-objects] [--bare]
           [--git-dir=<path>] [--work-tree=<path>] [--namespace=<name>]
           <command> [<args>]

These are common Git commands used in various situations:

start a working area (see also: git help tutorial)
   clone      Clone a repository into a new directory
   init       Create an empty Git repository or reinitialize an existing one

work on the current change (see also: git help everyday)
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   mv         Move or rename a file, a directory, or a symlink
   reset      Reset current HEAD to the specified state
   rm         Remove files from the working tree and from the index

examine the history and state (see also: git help revisions)
   bisect     Use binary search to find the commit that introduced a bug
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collaborate (see also: git help workflows)
   fetch      Download objects and refs from another repository
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   push       Update remote refs along with associated objects

'git help -a' and 'git help -g' list available subcommands and some
concept guides. See 'git help <command>' or 'git help <concept>'
to read about a specific subcommand or concept.

	Type	Days for shipping (real)	Days for shipment (scheduled)	Benefit per order	Sales per customer	Delivery Status	Late_delivery_risk	Category Id	Category Name	Customer City	Customer Country	Customer Email	Customer Fname	Customer Id	Customer Lname	Customer Password	Customer Segment	Customer State	Customer Street	Customer Zipcode	Department Id	Department Name	Latitude	Longitude	Market	Order City	Order Country	Order Customer Id	order date (DateOrders)	Order Id	Order Item Cardprod Id	Order Item Discount	Order Item Discount Rate	Order Item Id	Order Item Product Price	Order Item Profit Ratio	Order Item Quantity	Sales	Order Item Total	Order Profit Per Order	Order Region	Order State	Order Status	Order Zipcode	Product Card Id	Product Category Id	Product Description	Product Image	Product Name	Product Price	shipping date (DateOrders)	Shipping Mode
0	DEBIT	3	4	91.250000	314.640015	Advance shipping	0	73	Sporting Goods	Caguas	Puerto Rico	XXXXXXXXX	Cally	20755	Holloway	XXXXXXXXX	Consumer	PR	5365 Noble Nectar Island	725.0	2	Fitness	18.251453	-66.037056	Pacific Asia	Bekasi	Indonesia	20755	1/31/2018 22:56	77202	1360	13.110000	0.04	180517	327.75	0.29	1	327.75	314.640015	91.250000	Southeast Asia	Java Occidental	COMPLETE	NaN	1360	73	NaN	http://images.acmesports.sports/Smart+watch	Smart watch	327.75	2/3/2018 22:56	Standard Class
1	TRANSFER	5	4	-249.089996	311.359985	Late delivery	1	73	Sporting Goods	Caguas	Puerto Rico	XXXXXXXXX	Irene	19492	Luna	XXXXXXXXX	Consumer	PR	2679 Rustic Loop	725.0	2	Fitness	18.279451	-66.037064	Pacific Asia	Bikaner	India	19492	1/13/2018 12:27	75939	1360	16.389999	0.05	179254	327.75	-0.80	1	327.75	311.359985	-249.089996	South Asia	Rajast�n	PENDING	NaN	1360	73	NaN	http://images.acmesports.sports/Smart+watch	Smart watch	327.75	1/18/2018 12:27	Standard Class
2	CASH	4	4	-247.779999	309.720001	Shipping on time	0	73	Sporting Goods	San Jose	EE. UU.	XXXXXXXXX	Gillian	19491	Maldonado	XXXXXXXXX	Consumer	CA	8510 Round Bear Gate	95125.0	2	Fitness	37.292233	-121.881279	Pacific Asia	Bikaner	India	19491	1/13/2018 12:06	75938	1360	18.030001	0.06	179253	327.75	-0.80	1	327.75	309.720001	-247.779999	South Asia	Rajast�n	CLOSED	NaN	1360	73	NaN	http://images.acmesports.sports/Smart+watch	Smart watch	327.75	1/17/2018 12:06	Standard Class
3	DEBIT	3	4	22.860001	304.809998	Advance shipping	0	73	Sporting Goods	Los Angeles	EE. UU.	XXXXXXXXX	Tana	19490	Tate	XXXXXXXXX	Home Office	CA	3200 Amber Bend	90027.0	2	Fitness	34.125946	-118.291016	Pacific Asia	Townsville	Australia	19490	1/13/2018 11:45	75937	1360	22.940001	0.07	179252	327.75	0.08	1	327.75	304.809998	22.860001	Oceania	Queensland	COMPLETE	NaN	1360	73	NaN	http://images.acmesports.sports/Smart+watch	Smart watch	327.75	1/16/2018 11:45	Standard Class
4	PAYMENT	2	4	134.210007	298.250000	Advance shipping	0	73	Sporting Goods	Caguas	Puerto Rico	XXXXXXXXX	Orli	19489	Hendricks	XXXXXXXXX	Corporate	PR	8671 Iron Anchor Corners	725.0	2	Fitness	18.253769	-66.037048	Pacific Asia	Townsville	Australia	19489	1/13/2018 11:24	75936	1360	29.500000	0.09	179251	327.75	0.45	1	327.75	298.250000	134.210007	Oceania	Queensland	PENDING_PAYMENT	NaN	1360	73	NaN	http://images.acmesports.sports/Smart+watch	Smart watch	327.75	1/15/2018 11:24	Standard Class

	Type	Benefit per order	Sales per customer	Delivery Status	Late_delivery_risk	Category Name	Customer City	Customer Country	Customer Id	Customer Segment	Customer State	Customer Zipcode	Department Name	Latitude	Longitude	Market	Order City	Order Country	Order Customer Id	order date (DateOrders)	Order Id	Order Item Cardprod Id	Order Item Discount	Order Item Discount Rate	Order Item Id	Order Item Product Price	Order Item Profit Ratio	Order Item Quantity	Sales	Order Item Total	Order Profit Per Order	Order Region	Order State	Order Status	Order Zipcode	Product Card Id	Product Category Id	Product Description	Product Image	Product Name	Product Price	shipping date (DateOrders)	Shipping Mode
0	DEBIT	91.250000	314.640015	Advance shipping	0	Sporting Goods	Caguas	Puerto Rico	20755	Consumer	PR	725.0	Fitness	18.251453	-66.037056	Pacific Asia	Bekasi	Indonesia	20755	1/31/2018 22:56	77202	1360	13.110000	0.04	180517	327.75	0.29	1	327.75	314.640015	91.250000	Southeast Asia	Java Occidental	COMPLETE	NaN	1360	73	NaN	http://images.acmesports.sports/Smart+watch	Smart watch	327.75	2/3/2018 22:56	Standard Class
1	TRANSFER	-249.089996	311.359985	Late delivery	1	Sporting Goods	Caguas	Puerto Rico	19492	Consumer	PR	725.0	Fitness	18.279451	-66.037064	Pacific Asia	Bikaner	India	19492	1/13/2018 12:27	75939	1360	16.389999	0.05	179254	327.75	-0.80	1	327.75	311.359985	-249.089996	South Asia	Rajast�n	PENDING	NaN	1360	73	NaN	http://images.acmesports.sports/Smart+watch	Smart watch	327.75	1/18/2018 12:27	Standard Class
2	CASH	-247.779999	309.720001	Shipping on time	0	Sporting Goods	San Jose	EE. UU.	19491	Consumer	CA	95125.0	Fitness	37.292233	-121.881279	Pacific Asia	Bikaner	India	19491	1/13/2018 12:06	75938	1360	18.030001	0.06	179253	327.75	-0.80	1	327.75	309.720001	-247.779999	South Asia	Rajast�n	CLOSED	NaN	1360	73	NaN	http://images.acmesports.sports/Smart+watch	Smart watch	327.75	1/17/2018 12:06	Standard Class
3	DEBIT	22.860001	304.809998	Advance shipping	0	Sporting Goods	Los Angeles	EE. UU.	19490	Home Office	CA	90027.0	Fitness	34.125946	-118.291016	Pacific Asia	Townsville	Australia	19490	1/13/2018 11:45	75937	1360	22.940001	0.07	179252	327.75	0.08	1	327.75	304.809998	22.860001	Oceania	Queensland	COMPLETE	NaN	1360	73	NaN	http://images.acmesports.sports/Smart+watch	Smart watch	327.75	1/16/2018 11:45	Standard Class
4	PAYMENT	134.210007	298.250000	Advance shipping	0	Sporting Goods	Caguas	Puerto Rico	19489	Corporate	PR	725.0	Fitness	18.253769	-66.037048	Pacific Asia	Townsville	Australia	19489	1/13/2018 11:24	75936	1360	29.500000	0.09	179251	327.75	0.45	1	327.75	298.250000	134.210007	Oceania	Queensland	PENDING_PAYMENT	NaN	1360	73	NaN	http://images.acmesports.sports/Smart+watch	Smart watch	327.75	1/15/2018 11:24	Standard Class

	Order Country	Order City	Profit of Orders
3007	Rep�blica Dominicana	Santo Domingo	23836.099951
2540	M�xico	Mexico City	15319.390033
1976	Honduras	Tegucigalpa	14041.099944
2606	Nicaragua	Managua	13914.150000
2904	Reino Unido	London	12220.279964
...	...	...	...
2173	Irlanda	Dubl�n	-1662.270040
686	China	Dalian	-1716.799995
130	Alemania	Schwerin	-2081.249994
2893	Reino Unido	Kettering	-2275.250000
2860	Reino Unido	Dudley	-2951.779979

	Type	Benefit per order	Sales per customer	Delivery Status	Late_delivery_risk	Category Name	Customer City	Customer Country	Customer Id	Customer Segment	Customer State	Customer Zipcode	Department Name	Latitude	Longitude	Market	Order City	Order Country	Order Customer Id	order date (DateOrders)	Order Id	Order Item Cardprod Id	Order Item Discount	Order Item Discount Rate	Order Item Id	Order Item Product Price	Order Item Profit Ratio	Sales	Order Item Total	Order Profit Per Order	Order Region	Order State	Order Zipcode	Product Card Id	Product Category Id	Product Description	Product Image	Product Name	Product Price	shipping date (DateOrders)	Shipping Mode	Customer_ID_STR
0	1	18934	2568	0	0	40	66	1	20649	0	36	7	4	3624	4420	3	331	70	20649	5961	65749	114	426	4	180516	62	140	166	2568	18934	15	475	12733	114	47	0	78	78	62	27149	3	11912
1	3	2272	2559	1	1	40	66	1	19386	0	36	7	4	5522	4419	3	391	69	19386	1147	64486	114	498	5	179253	62	44	166	2559	2272	13	841	100315	114	47	120339	78	78	62	2209	3	10509
2	0	2293	2555	3	0	40	452	0	19385	0	5	941	4	9146	232	3	391	69	19385	1146	64485	114	529	6	179252	62	44	166	2555	2293	13	841	100316	114	47	120340	78	78	62	1980	3	10508
3	1	13638	2546	0	0	40	285	0	19384	2	5	720	4	8467	663	3	3226	8	19384	1145	64484	114	600	7	179251	62	119	166	2546	13638	11	835	100317	114	47	120341	78	78	62	1752	3	10507
4	2	20599	2526	0	0	40	66	1	19383	1	36	7	4	3783	4421	3	3226	8	19383	1144	64483	114	682	8	179250	62	156	166	2526	20599	11	835	100318	114	47	120342	78	78	62	1528	3	10505

	actual	predicted
116587	0	0
36340	0	0
175763	0	0
96918	0	0
71197	0	0

Deleting duplicate and not reated features¶

Data Visualization¶

Delvery status¶

Top 20 Customers regarding the quanitity of orders¶

Top 20 Customers regarding profit of all orders¶

Customer Segment¶

Category¶

Geo Features¶

Sales Analysis¶

Date and sales analysis¶

Forecasting¶

Predicting if an order is fraud or not¶

We can consider NaN values as a separte class using LabelEncoder¶

Deleting Features which high-correlated with other features to avoid multicollinarity¶

Remove all features which have less than +-0.004 correlation with target¶

Customer ZipCode and Customer state have high correlation with Customer Country. we can remove them and keep only customer country¶

Forecasting of an order has tardiness¶

Forecasting Sales of different products¶

	Type	Benefit per order	Sales per customer	Delivery Status	Late_delivery_risk	Category Name	Customer City	Customer Country	Customer Id	Customer Segment	Customer State	Customer Zipcode	Department Name	Latitude	Longitude	Market	Order City	Order Country	order date (DateOrders)	Order Id	Order Item Cardprod Id	Order Item Discount	Order Item Discount Rate	Order Item Product Price	Order Item Quantity	Order Region	Order State	Order Zipcode	Product Description	Product Image	Product Status	Shipping Mode	Customer_ID_STR
0	0.202094	-0.002586	-0.000807	0.128768	-0.167158	-0.002104	-0.003561	0.006756	0.009375	-0.005935	0.005670	-0.005600	-0.000837	-0.004709	0.005100	-0.000293	-0.005889	-0.004928	0.003002	0.002222	-0.001109	0.002893	0.002271	0.000315	-0.000757	0.009077	-0.006105	-4.274182e-02	-5.615812e-02	-0.000741	NaN	-0.004641	0.000490
1	0.000000	0.271923	0.731807	0.000000	0.000000	0.371352	0.130283	0.004098	0.000068	0.011686	0.016001	0.017337	0.722187	0.045427	0.030231	0.900798	0.012345	0.036290	0.202217	0.345187	0.637360	0.219059	0.334515	0.893383	0.747677	0.000115	0.009491	9.230621e-74	5.077551e-126	0.752963	NaN	0.048639	0.835012

	Variable	F_Value	P_Value
0	Type	7686.615869	0.000
18	Order Customer Id	15.868141	0.000
3	Delivery Status	3043.655705	0.000
4	Late_delivery_risk	5188.953532	0.000
33	Order Zipcode	330.383488	0.000
8	Customer Id	15.868141	0.000
31	Order Region	14.875551	0.000
36	Product Description	571.103804	0.000
7	Customer Country	8.239934	0.004
32	Order State	6.728235	0.009
16	Order City	6.260807	0.012
9	Customer Segment	6.358072	0.012
10	Customer State	5.802926	0.016
11	Customer Zipcode	5.662017	0.017
14	Longitude	4.696175	0.030
17	Order Country	4.383497	0.036
13	Latitude	4.002769	0.045
42	Shipping Mode	3.887812	0.049
41	shipping date (DateOrders)	3.875811	0.049

	Variable	F_Value	P_Value
0	Type	685.999651	0.000
30	Product Description	72.142896	0.000
27	Order Zipcode	25.191994	0.000
36	Shipping Mode	34666.398337	0.000
3	Delivery Status	6798.252477	0.000
25	Order Region	6.848744	0.009
5	Customer City	4.662629	0.031
35	shipping date (DateOrders)	3.556568	0.059

	Variable	F_Value	P_Value
20	Order Id	1165.171704	0.000
22	Order Item Discount	57166.125441	0.000
21	Order Item Cardprod Id	12782.968321	0.000
39	shipping date (DateOrders)	142.652140	0.000
19	order date (DateOrders)	128.461963	0.000
18	Order Customer Id	673.464036	0.000
27	Order Profit Per Order	13782.670150	0.000
15	Market	240.910781	0.000
28	Order Region	140.517795	0.000
29	Order State	27.935380	0.000
26	Order Item Total	481682.347274	0.000
12	Department Name	524.094617	0.000
32	Product Card Id	12782.968321	0.000
8	Customer Id	673.464036	0.000
33	Product Category Id	10095.780861	0.000
35	Product Image	37751.723070	0.000
5	Category Name	26066.331991	0.000
36	Product Name	37751.723070	0.000
37	Product Price	116680.120560	0.000
2	Sales per customer	481682.347274	0.000
1	Benefit per order	13782.670150	0.000
24	Order Item Id	1133.743612	0.000
16	Order City	8.764240	0.003
9	Customer Segment	4.266892	0.039
31	Order Zipcode	4.200315	0.040

	actual	predicted
99963	149.940002	149.975128
63538	210.850006	210.474564
6661	179.970001	179.993896
93913	129.990005	129.989929
90626	129.990005	129.990906

	actual	predicted
80120	199.990005	199.987198
19670	250.000000	249.987595
114887	249.899994	249.878433
120110	299.980011	299.969971
56658	119.970001	119.918625

Deleting duplicate and not reated features¶

Data Visualization¶

Delvery status¶

Top 20 Customers regarding the quanitity of orders¶

Top 20 Customers regarding profit of all orders¶

Customer Segment¶

Category¶

Geo Features¶

Sales Analysis¶

Date and sales analysis¶

Forecasting¶

Predicting if an order is fraud or not¶

We can consider NaN values as a separte class using LabelEncoder¶

Deleting Features which high-correlated with other features to avoid multicollinarity¶

Remove all features which have less than +-0.004 correlation with target¶

Customer ZipCode and Customer state have high correlation with Customer Country. we can remove them and keep only customer country¶

Forecasting of an order has tardiness¶

'Delivery Status' should also be omited. It has directly related to the target¶

Forecasting Sales of different products¶